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Abstract Background Soil-transmitted helminthiasis (STH) are a parasitic infection that predominantly affects impoverished regions. Model-based geostatistics (MBG) has been established as a set of modern statistical methods that enable mapping of disease risk in a geographical area of interest. We investigate how the use of remotely sensed covariates can help to improve the predictive inferences on STH prevalence using MBG methods. In particular, we focus on how the covariates impact on the classification of areas into distinct class of STH prevalence. Methods This study uses secondary data obtained from a sample of 1551 schools in Kenya, gathered through a combination of longitudinal and cross-sectional surveys. We compare the performance of two geostatistical models: one that does not make use of any spatially referenced covariate; and a second model that uses remotely sensed covariates to assist STH prevalence prediction. We also carry out a simulation study in which we compare the performance of the two models in the classifications of areal units with varying sample sizes and prevalence levels. Results The model with covariates generated lower levels of uncertainty and was able to classify 88 more districts into prevalence classes than the model without covariates, which instead left those as “unclassified”. The simulation study showed that the model with covariates also yielded a higher proportion of correct classification of at least 40% for all sub-counties. Conclusion Covariates can substantially reduce the uncertainty of the predictive inference generated from geostatistical models. Using covariates can thus contribute to the design of more effective STH control strategies by reducing sample sizes without compromising the predictive performance of geostatistical models.

El presente trabajo explora el acoso sexual callejero en el espacio público en el caso Riberas del Bravo, Ciudad Juárez, México. Esta es una de las zonas con mayor incidencia de violencia sexual en el Estado y guarda relación con una alta incidencia a nivel nacional. En relación con la metodología, se trabajó con siete mujeres que viven en la zona y se desplazan solitarias a distintos lugares de trabajo, escuela y recreación. Se utilizaron densidades de núcleo como herramienta cartográfica para identificar los espacios representados por las participantes como peligrosos y de incidencia de acoso sexual callejero, así como de equipamiento existente. A partir de ello y de categorías teóricas construidas por la sociología urbana, los estudios de género y la perspectiva territorial, se encontró que, en espacios cercanos a su vivienda, baldíos semi-amurallados y tiendas de conveniencia es donde el acoso cuenta con la mayor incidencia –abarcando desde comentarios sexuales hasta persecución–, siendo realizado en grupos de dos o más hombres. La frecuencia va desde un seguido y un muy seguido, siendo los fines de semana por la noche los momentos de mayor incidencia. En estos casos la reacción principal es caminar de prisa y evitar la confrontación. Por lo tanto, el acoso reconfigura la interacción espacial pública de las mujeres y genera una segregación en escalas de diferenciación de uso espacial.

Abstract Salmonella Typhi is a human-restricted pathogen that is transmitted by the faecal–oral route and causative organism of typhoid fever. Using health facility data from 2016 to 2020, this study focuses on modelling the spatial variation in typhoid risk in Ndirande township in Blantyre. To pursue this objective, we developed a marked inhomogeneous Poisson process model that allows us to incorporate both individual-level and environmental risk factors. The results from our analysis indicate that typhoid cases are spatially clustered, with the incidence decreasing by 54% for a unit increase in the water, sanitation, and hygiene (WASH) score. Typhoid intensity was also higher in children aged below 18 years than in adults. However, our results did not show evidence of a strong temporal variation in typhoid incidence. We also discuss the inferential benefits of using point pattern models to characterise the spatial variation in typhoid risk and outline possible extensions of the proposed modelling framework.

Rats are major reservoirs for pathogenic Leptospira, the bacteria causing leptospirosis, particularly in urban informal settlements. However, the impact of variation in rat abundance and pathogen shedding rates on spillover transmission to humans remains unclear. This study aimed to investigate how spatial variation in reservoir abundance and pathogen pressure affect Leptospira spillover transmission to humans in a Brazilian urban informal settlement. A longitudinal eco-epidemiological study was conducted from 2013 to 2014 to characterize the spatial distribution of rat abundance and Leptospira shedding rates in rats and determine the association with human infection risk in a cohort of 2,206 community residents. Tracking plates and live-trapping were used to measure rat abundance and quantify rat shedding status and load. In parallel, four sequential biannual serosurveys were used to identify human Leptospira infections. To evaluate the role of shedding on human risk, we built three statistical models for: (1) the relative abundance of rats, (2) the shedding rate by individual rats, and (3) human Leptospira infection, in which “total shedding”, obtained by multiplying the predictions from those two models, was used as a risk factor. We found that Leptospira shedding was associated with older and sexually mature rats and varied spatially and temporally—higher at valley bottoms and with seasonal rainfall (December to March). The point estimate for “total shedding” by rat populations was positive, i.e., Leptospira infection risk increased with total shedding, but the association was not significant [odds ratio (OR) = 1.1; 95% confidence interval (CI): 0.9, 1.4]. This positive trend was mainly driven by rat abundance, rather than individual rat shedding (OR = 1.8; 95% CI: 0.6, 5.4 vs. OR = 1.0; 95% CI: 0.7, 1.4]. Infection risk was higher in areas with more vegetative land cover (OR = 2.4; 95% CI: 1.2, 4.8), and when floodwater entered the house (OR = 2.4; 95% CI: 1.6, 3.4). Our findings indicate that environmental and hydrological factors play a more significant role in Leptospira spillover than rat associated factors. Furthermore, we developed a novel approach combining several models to elucidate complex links between animal reservoir abundance, pathogen shedding and environmental factors on zoonotic spillover in humans that can be extended to other environmentally transmitted diseases.

Abstract Background Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. Methods Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. Results The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. Conclusions Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care.

BackgroundIn 2020 the World Health Organization (WHO) declared that Malawi had successfully eliminated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distributions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national database and map of LF clinical cases across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information.MethodologyLF clinical cases identified through programme house-to-house surveys across 90 sub-district administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia prevalence spatial estimates.Principle findingsThe models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country.ConclusionsThe models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies.

Abstract Objective: To establish the prevalence of double burden of malnutrition (DBM) and triple burden of malnutrition (TBM) among mother–child pairs in Malawi and explore their geographical distribution and associated multilevel factors. Design: Cross-sectional study using secondary data from the 2015–2016 Malawi Demographic and Health Survey using a mixed effects binomial model to identify multilevel factors associated with DBM and TBM. Georeferenced covariates were used to map the predicted prevalence of DBM and TBM. Setting: All twenty-eight districts in Malawi. Participants: Mother–child pairs with mothers aged 15–49 years and children aged below 60 months (n 4618 pairs) for DBM and between 6 and 59 months (n 4209 pairs) for TBM. Results: Approximately 5·5 % (95% confidence interval (CI): 4·7 %, 6·4 %) of mother–child pairs had DBM, and 3·1 % (95 % CI: 2·5 %, 4·0 %) had TBM. The subnational-level prevalence of DBM and TBM was highest in cities. The adjusted odds of DBM were threefold higher (adjusted Odds Ratio, AOR: 2·8, 95 % CI: 1·1, 7·3) with a higher proportion of wealthy households in a community. The adjusted odds of TBM were 60 % lower (AOR: 0·4; 95 % CI: 0·2, 0·8) among pairs where the women had some education compared with women with no education. Conclusions: Although the prevalence of DBM and TBM is currently low in Malawi, it is more prevalent in pairs with women with no education and in relatively wealthier communities. Targeted interventions should address both maternal overnutrition and child undernutrition in cities and these demographics.

Malaria remains a major health problem in developing countries despite a significant reduction in incidence in the last few years. Disease mapping thus helps to understand the spatial pattern and identify areas characterized by unusual risks. Several spatial models have been used to analyze the incidence of malaria. We aim to compare the predictive performance of these models and investigate the effect of ignoring spatial correlation. The reported malaria case counts of genus P.falciparum in 149 districts of southern Ethiopia from January 2016 to May 2019 were analyzed using the spatial time series model (STS) that ignores spatial correlation, Spatio-temporal conditional autoregressive model (STCAR), Spatio-temporal geostatistical model (STG) and Spatio-temporal spatial discrete approximation to log Gaussian cox process (STSDALGCP). We assess the predictive performance of the models using root mean square error, mean absolute error, and coverage probability. We found that monthly average rainfall, temperature, humidity, and EVI are significantly associated with malaria risk. The spatial variation of malaria incidence changes with time, in particular, the high incidence was observed from November to December, months after heavy rainfall, and more pronounced in the southwest of the country. STSDALGCP gives a small prediction error in test set and captures the uncertainties better than other models, while the STS model gives a high prediction error. Accounting for spatial correlation is crucial for disease risk mapping and leads to better prediction of disease risk. Since malaria transmission operates in a spatially continuous manner, a spatially continuous model should be considered when it is computationally feasible.

Abstract Background Rapid determination of an individual’s antibody status can be beneficial in understanding an individual’s immune response to SARS-CoV-2 and for initiation of therapies that are only deemed effective in sero-negative individuals. Antibody lateral flow tests (LFTs) have potential to address this need as a rapid, point of care test. Methods Here we present a proof-of-concept evaluation of eight LFT brands using sera from 95 vaccinated individuals to determine sensitivity for detecting vaccination generated antibodies. Samples were analysed on eight different brands of antibody LFT and an automated chemiluminescent microparticle immunoassay (CMIA) that identifies anti-spike antibodies which was used as our reference standard. Results All 95 (100%) participants tested positive for anti-spike antibodies by the chemiluminescent microparticle immunoassay (CMIA) reference standard post-dose two of their SARS-CoV-2 vaccine: BNT162b2 (Pfizer/BioNTech, n = 60), AZD1222 (AstraZeneca, n = 31), mRNA-1273 (Moderna, n = 2) and Undeclared Vaccine Brand (n = 2). Sensitivity increased from dose one to dose two in six out of eight LFTs with three tests achieving 100% sensitivity at dose two in detecting anti-spike antibodies. Conclusions These tests are demonstrated to be highly sensitive to detect raised antibody levels in vaccinated individuals. RDTs are low cost and rapid alternatives to ELISA based systems.

Abstract Background Declines in malaria burden in Uganda have slowed. Modelling predicts that indoor residual spraying (IRS) and mass drug administration (MDA), when co-timed, have synergistic impact. This study investigated additional protective impact of population-based MDA on malaria prevalence, if any, when added to IRS, as compared with IRS alone and with standard of care (SOC). Methods The 32-month quasi-experimental controlled before-and-after trial enrolled an open cohort of residents (46,765 individuals, 1st enumeration and 52,133, 4th enumeration) of Katakwi District in northeastern Uganda. Consented participants were assigned to three arms based on residential subcounty at study start: MDA+IRS, IRS, SOC. IRS with pirimiphos methyl and MDA with dihydroartemisinin- piperaquine were delivered in 4 co-timed campaign-style rounds 8 months apart. The primary endpoint was population prevalence of malaria, estimated by 6 cross-sectional surveys, starting at baseline and preceding each subsequent round. Results Comparing malaria prevalence in MDA+IRS and IRS only arms over all 6 surveys (intention-to-treat analysis), roughly every 6 months post-interventions, a geostatistical model found a significant additional 15.5% (95% confidence interval (CI): [13.7%, 17.5%], Z = 9.6, p = 5e−20) decrease in the adjusted odds ratio (aOR) due to MDA for all ages, a 13.3% reduction in under 5’s (95% CI: [10.5%, 16.8%], Z = 4.02, p = 5e−5), and a 10.1% reduction in children 5–15 (95% CI: [8.5%, 11.8%], Z = 4.7, p = 2e−5). All ages residents of the MDA + IRS arm enjoyed an overall 80.1% reduction (95% CI: [80.0%, 83.0%], p = 0.0001) in odds of qPCR confirmed malaria compared with SOC residents. Secondary difference-in-difference analyses comparing surveys at different timepoints to baseline showed aOR (MDA + IRS vs IRS) of qPCR positivity between 0.28 and 0.66 (p < 0.001). Of three serious adverse events, one (nonfatal) was considered related to study medications. Limitations include the initial non-random assignment of study arms, the single large cluster per arm, and the lack of an MDA-only arm, considered to violate equipoise. Conclusions Despite being assessed at long time points 5–7 months post-round, MDA plus IRS provided significant additional protection from malaria infection over IRS alone. Randomized trials of MDA in large areas undergoing IRS recommended as well as cohort studies of impact on incidence. Trial registration: This trial was retrospectively registered 11/07/2018 with the Pan African Clinical Trials Registry (PACTR201807166695568).

Introduction Leptospirosis is a globally distributed zoonotic and environmentally mediated disease that has emerged as a major health problem in urban slums in developing countries. Its aetiological agent is bacteria of the genus Leptospira, which are mainly spread in the urine of infected rodents, especially in an environment where adequate sanitation facilities are lacking, and it is known that open sewers are key transmission sources of the disease. Therefore, we aim to evaluate the effectiveness of a simplified sewerage intervention in reducing the risk of exposure to contaminated environments and Leptospira infection and to characterise the transmission mechanisms involved.Methods and analysis This matched quasi-experimental study design using non-randomised intervention and control clusters was designed to assess the effectiveness of an urban simplified sewerage intervention in the low-income communities of Salvador, Brazil. The intervention consists of household-level piped sewerage connections and community engagement and public involvement activities. A cohort of 1400 adult participants will be recruited and grouped into eight clusters consisting of four matched intervention-control pairs with approximately 175 individuals in each cluster in baseline. The primary outcome is the seroincidence of Leptospira infection assessed through five serological measurements: one preintervention (baseline) and four postintervention. As a secondary outcome, we will assess Leptospira load in soil, before and after the intervention. We will also assess Leptospira exposures before and after the intervention, through transmission modelling, accounting for residents' movement, contact with flooding, contaminated soil and water, and rat infestation, to examine whether and how routes of exposure for Leptospira change following the introduction of sanitation.Ethics and dissemination This study protocol has been reviewed and approved by the ethics boards at the Federal University of Bahia and the Brazilian National Research Ethics Committee. Results will be disseminated through peer-reviewed publications and presentations to implementers, researchers and participating communities.Trial registration number Brazilian Clinical Trials Registry (RBR-8cjjpgm).

Abstract Background Impact evaluation of most water, sanitation and hygiene (WASH) interventions in health are user-centered. However, recent research discussed WASH herd protection – community WASH coverage could protect neighboring households. We evaluated the effect of water and sanitation used in the household and by household neighbors in children’s morbidity and mortality using recorded health data. Methods We conducted a retrospective cohort including 61,333 children from a district in Mozambique during 2012–2015. We obtained water and sanitation household data and morbidity data from Manhiça Health Research Centre surveillance system. To evaluate herd protection, we estimated the density of household neighbors with improved facilities using a Kernel Density Estimator. We fitted negative binomial adjusted regression models to assess the minimum children-based incidence rates for every morbidity indicator, and Cox regression models for mortality. Results Household use of unimproved water and sanitation displayed a higher rate of outpatient visit, diarrhea, malaria, and anemia. Households with unimproved water and sanitation surrounded by neighbors with improved water and sanitation high coverage were associated with a lower rate of outpatient visit, malaria, anemia, and malnutrition. Conclusion Household and neighbors’ access to improve water and sanitation can affect children’s health. Accounting for household WASH and herd protection in interventions’ evaluation could foster stakeholders’ investment and improve WASH related diseases control. Graphical Abstract Distribution of main water and sanitation facilities used during study period.

Abstract Severe acute respiratory coronavirus 2 (SARS-CoV-2) has spread globally since its emergence in 2019. Most SARS-CoV-2 infections generate immune responses leading to rising levels of immunoglobulins (Ig) M, A and G which can be detected using diagnostic tests including enzyme-linked immunosorbent assays (ELISA). Whilst implying previous SARS-CoV-2 infection, the detection of Ig by ELISA does not guarantee the presence of neutralising antibodies (NAb) that can prevent the virus infecting cells. Plaque reduction neutralisation tests (PRNT) detect NAb, but are not amenable to mass testing as they take several days and require use of SARS-CoV-2 in high biocontainment laboratories. We evaluated the ability of IgG and IgM ELISAs targeting SARS-CoV-2 spike subunit 1 receptor binding domain (S1-RBD), and spike subunit 2 (S2) and nucleocapsid protein (NP), at predicting the presence and magnitude of NAb determined by PRNT. IgG S2 + NP ELISA was 96.8% [95% CI 83.8–99.9] sensitive and 88.9% [95% CI 51.8–99.7] specific at predicting the presence of NAbs (PRNT80 > 1:40). IgG and IgM S1-RBD ELISAs correlated with PRNT titre, with higher ELISA results increasing the likelihood of a robust neutralising response. The IgM S1-RBD assay can be used as a rapid, high throughput test to approximate the magnitude of NAb titre.

Abstract Background In malaria serology analysis, the standard approach to obtain seroprevalence, i.e the proportion of seropositive individuals in a population, is based on a threshold which is used to classify individuals as seropositive or seronegative. The choice of this threshold is often arbitrary and is based on methods that ignore the age-dependency of the antibody distribution. Methods Using cross-sectional antibody data from the Western Kenyan Highlands, this paper introduces a novel approach that has three main advantages over the current threshold-based approach: it avoids the use of thresholds; it accounts for the age dependency of malaria antibodies; and it allows us to propagate the uncertainty from the classification of individuals into seropositive and seronegative when estimating seroprevalence. The reversible catalytic model is used as an example for illustrating how to propagate this uncertainty into the parameter estimates of the model. Results This paper finds that accounting for age-dependency leads to a better fit to the data than the standard approach which uses a single threshold across all ages. Additionally, the paper also finds that the proposed threshold-free approach is more robust against the selection of different age-groups when estimating seroprevalence. Conclusion The novel threshold-free approach presented in this paper provides a statistically principled and more objective approach to estimating malaria seroprevalence. The introduced statistical framework also provides a means to compare results across studies which may use different age ranges for the estimation of seroprevalence.

Abstract Background Model-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. However, the wider adoption of MBG methods by national control programmes to inform health policy decisions is hindered by the lack of advanced statistical expertise and suitable computational equipment. Here, Maplaria, an interactive, user-friendly web-application that allows users to upload their own malaria prevalence data and carry out geostatistical prediction of annual malaria prevalence at any desired spatial scale, is introduced. Methods In the design of the Maplaria web application, two main criteria were considered: the application should be able to classify subnational divisions into the most likely endemicity levels; the web application should allow only minimal input from the user in the set-up of the geostatistical inference process. To achieve this, the process of fitting and validating the geostatistical models is carried out by statistical experts using publicly available malaria survey data from the Harvard database. The stage of geostatistical prediction is entirely user-driven and allows the user to upload malaria data, as well as vector data that define the administrative boundaries for the generation of spatially aggregated inferences. Results The process of data uploading and processing is split into a series of steps spread across screens through the progressive disclosure technique that prevents the user being immediately overwhelmed by the length of the form. Each of these is illustrated using a data set from the Malaria Indicator carried out in Tanzania in 2017 as an example. Conclusions Maplaria application provides a user-friendly solution to the problem making geostatistical methods more accessible to users that have not undertaken formal training in statistics. The application is a useful tool that can be used to foster ownership, among policy makers, of disease risk maps and promote better use of data for decision-making in low resource settings.

Abstract The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner.

Background: Zoonotic spillover from animal reservoirs is responsible for a significant global public health burden, but the processes that promote spillover events are poorly understood in complex urban settings. Endemic transmission of Leptospira, the agent of leptospirosis, in marginalised urban communities occurs through human exposure to an environment contaminated by bacteria shed in the urine of the rat reservoir. However, it is unclear to what extent transmission is driven by variation in the distribution of rats or by the dispersal of bacteria in rainwater runoff and overflow from open sewer systems. Methods: We conducted an eco-epidemiological study in a high-risk community in Salvador, Brazil, by prospectively following a cohort of 1401 residents to ascertain serological evidence for leptospiral infections. A concurrent rat ecology study was used to collect information on the fine-scale spatial distribution of ‘rattiness’, our proxy for rat abundance and exposure of interest. We developed and applied a novel geostatistical framework for joint spatial modelling of multiple indices of disease reservoir abundance and human infection risk. Results: The estimated infection rate was 51.4 (95%CI 40.4, 64.2) infections per 1000 follow-up events. Infection risk increased with age until 30 years of age and was associated with male gender. Rattiness was positively associated with infection risk for residents across the entire study area, but this effect was stronger in higher elevation areas (OR 3.27 95% CI 1.68, 19.07) than in lower elevation areas (OR 1.14 95% CI 1.05, 1.53). Conclusions: These findings suggest that, while frequent flooding events may disperse bacteria in regions of low elevation, environmental risk in higher elevation areas is more localised and directly driven by the distribution of local rat populations. The modelling framework developed may have broad applications in delineating complex animal-environment-human interactions during zoonotic spillover and identifying opportunities for public health intervention. Funding: This work was supported by the Oswaldo Cruz Foundation and Secretariat of Health Surveillance, Brazilian Ministry of Health, the National Institutes of Health of the United States (grant numbers F31 AI114245, R01 AI052473, U01 AI088752, R01 TW009504 and R25 TW009338); the Wellcome Trust (102330/Z/13/Z), and by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB/JCB0020/2016). MTE was supported by a Medical Research UK doctorate studentship. FBS participated in this study under a FAPESB doctorate scholarship.

Abstract Micronutrient deficiencies and stunting rates are high in many low-income countries. Increasing and diversifying food intake are often challenging for small-scale farmers in lowland areas as flooding often results in crop losses and drowning of livestock. A cluster-randomised controlled trial was conducted over 12-months in Bangladesh, involving 150 small-scale duck rearing households, including 50 control, and 50 households each in two intervention arms. Interventions focussing on improving duck health and duck nutrition were applied on a village level. Data analysis focussed on assessing the impact of interventions on duck mortality, sales and consumption, and on dietary diversity of household members. Improved duck rearing increased the consumption and the sales of ducks. Household selling more ducks were more likely to purchase and consume milk products, contributing to an improved households’ dietary diversity. Our results suggest that improving duck rearing can provide a suitable and sustainable alternative to maintain and improve dietary diversity of households in flood-prone areas.

Abstract Background Life expectancy at birth (LEB), one of the main indicators of human longevity, has often been used to characterise the health status of a population. Understanding its relationships with the deprivation is key to develop policies and evaluate interventions that are aimed at reducing health inequalities. However, methodological challenges in the analysis of LEB data arise from the fact that different Government agencies often provide spatially aggregated information on LEB and the index of multiple deprivation (IMD) at different spatial scales. Our objective is to develop a geostatistical framework that, unlike existing methods of inference, allows to carry out spatially continuous prediction while dealing with spatial misalignment of the areal-level data. Methods We developed a model-based geostatistical approach for the joint analysis of LEB and IMD, when these are available over different partitions of the study region. We model the spatial correlation in LEB and IMD across the areal units using inter-point distances based on a regular grid covering the whole of the study area. The advantages and strengths of the new methodology are illustrated through an analysis of LEB and IMD data from the Liverpool district council. Results We found that the effect of IMD on LEB is stronger in males than in females, explaining about 63.35% of the spatial variation in LEB in the former group and 38.92% in the latter. We also estimate that LEB is about 8.5 years lower between the most and least deprived area of Liverpool for men, and 7.1 years for women. Finally, we find that LEB, both in males and females, is at least 80% likely to be above the England wide average only in some areas falling in the electoral wards of Childwall, Woolton and Church. Conclusion The novel model-based geostatistical framework provides a feasible solution to the spatial misalignment problem. More importantly, the proposed methodology has the following advantages over existing methods used model LEB: (1) it can deliver spatially continuous inferences using spatially aggregated data; (2) it can be applied to any form of misalignment with information provided at a range of spatial scales, from areal-level to pixel-level.

The elimination of onchocerciasis through community-based Mass Drug Administration (MDA) of ivermectin (Mectizan) is hampered by co-endemicity of Loa loa, as individuals who are highly co-infected with Loa loa parasites can suffer serious and occasionally fatal neurological reactions from the drug. The test-and-not-treat strategy of testing all individuals participating in MDA has some operational constraints including the cost and limited availability of LoaScope diagnostic tools. As a result, a Loa loa Antibody (Ab) Rapid Test was developed to offer a complementary way of determining the prevalence of loiasis. We develop a joint geostatistical modelling framework for the analysis of Ab and Loascope data to delineate whether an area is safe for MDA. Our results support the use of a two-stage strategy, in which Ab testing is used to identify areas that, with acceptably high probability, are safe or unsafe for MDA, followed by Loascope testing in areas whose safety status is uncertain. This work therefore contributes to the global effort towards the elimination of onchocerciasis as a public health problem by potentially reducing the time and cost required to establish whether an area is safe for MDA.

World Health Organization goals against soil-transmitted helminthiases (STH) are pointing towards seeking their elimination as a public health problem: reducing to less than 2% the proportion of moderate and heavy infections. Some regions are reaching WHO goals, but transmission could rebound if strategies are discontinued without an epidemiological evaluation. For that, sensitive diagnostic methods to detect low intensity infections and localization of ongoing transmission are crucial. In this work, we estimated and compared the STH infection as obtained by different diagnostic methods in a low intensity setting. We conducted a cross-sectional study enrolling 792 participants from a district in Mozambique. Two stool samples from two consecutive days were collected from each participant. Samples were analysed by Telemann, Kato-Katz and qPCR for STH detection. We evaluated diagnostic sensitivity using a composite reference standard. By geostatistical methods, we estimated neighbourhood prevalence of at least one STH infection for each diagnostic method. We used environmental, demographical and socioeconomical indicators to account for any existing spatial heterogeneity in infection. qPCR was the most sensitive technique compared to composite reference standard: 92% (CI: 83%– 97%) for A. lumbricoides, 95% (CI: 88%– 98%) for T. trichiura and 95% (CI: 91%– 97%) for hookworm. qPCR also estimated the highest neighbourhood prevalences for at least one STH infection in a low intensity setting. While 10% of the neighbourhoods showed a prevalence above 20% when estimating with single Kato-Katz from one stool and Telemann from one stool, 86% of the neighbourhoods had a prevalence above 20% when estimating with qPCR. In low intensity settings, STH estimated prevalence of infection may be underestimated if based on Kato-Katz. qPCR diagnosis outperformed the microscopy methods. Thus, implementation of qPCR based predictive maps at STH control and elimination programmes would disclose hidden transmission and facilitate targeted interventions for transmission interruption. Author summary Soil-transmitted helminths (STH) are parasitic worms present in over 1 billion people. Humans release STH eggs to the environment through faeces, and they become infected by egg ingestion or larvae skin penetration. The higher the number of eggs in an infection (high intensity infection) the higher the morbidity severity. For that, the World Health Organization (WHO) aims to eliminate STH as a health problem by reducing moderate and high intensity infections below 2% in 96% of endemic countries by 2030. STH infections main control strategies are mass drug administration to people at risk, and water, sanitation, and hygiene improvements. Nowadays, some regions are reaching WHO goals. Thus, tools to confirm if control strategies could be halted are needed. In this study, we selected a low intensity district in Southern Mozambique where we evaluated how different diagnostic techniques detect STH when intensity of infection is low. In addition, we also created district maps for STH estimated infection prevalence based on the different diagnostics assessed in order to identify the location of ongoing transmission. qPCR showed to be the most sensitive technique. Hence, maps based on the widely used Kato-Katz could underestimate the STH prevalence and lose hidden transmission.

Background: Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities. Methods: Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models. Results: Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR. Conclusions: Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices. Funding: This work was supported by Stichting Dioraphte grant number 13050800.

Current changes are making communities, cities, and territories increasingly vulnerable. Urban architectural interventions have the power to intervene this situation, directly reducing vulnerabilities or backing social initiatives. Urban and architectural interventions, however, are also those that take a longer time to be implemented and to impact society. For this, these interventions must be sustained by broad and transversal visions, as well as referring to the temporal context of the coming decades. For these reasons, the research project “Design for Vulnerables” aims to define which methodologies should be adopted to reduce urban vulnerabilities in the coming decades. A design workshop, set in a vulnerable community in the northern Mexico, was organized, documented, and analyzed. Based on the research by design methodology, the research highlighted current issues, transversal to urban-architectural design, which influence urban vulnerabilities. This multidisciplinary approach made it possible to generating a set of principles of Design for Vulnerables, graphically represented by a re-interpretation of the Krebs cycle.

BackgroundIndividuals infected with SARS-CoV-2 develop neutralising antibodies. We investigated the proportion of individuals with SARS-CoV-2 neutralising antibodies after infection and how this proportion varies with selected covariates.Methodology/principal findingsThis systematic review and meta-analysis examined the proportion of individuals with SARS-CoV-2 neutralising antibodies after infection and how these proportions vary with selected covariates. Three models using the maximum likelihood method assessed these proportions by study group, covariates and individually extracted data (protocol CRD42020208913). A total of 983 reports were identified and 27 were included. The pooled (95%CI) proportion of individuals with neutralising antibodies was 85.3% (83.5-86.9) using the titre cut off >1:20 and 83.9% (82.2-85.6), 70.2% (68.1-72.5) and 54.2% (52.0-56.5) with titres >1:40, >1:80 and >1:160, respectively. These proportions were higher among patients with severe COVID-19 (e.g., titres >1:80, 84.8% [80.0-89.2], >1:160, 74.4% [67.5-79.7]) than those with mild presentation (56.7% [49.9-62.9] and 44.1% [37.3-50.6], respectively) and lowest among asymptomatic infections (28.6% [17.9-39.2] and 10.0% [3.7-20.1], respectively). IgG and neutralising antibody levels correlated poorly.Conclusions/significance85% of individuals with proven SARS-CoV-2 infection had detectable neutralising antibodies. This proportion varied with disease severity, study setting, time since infection and the method used to measure antibodies.

Abstract Background Whilst previous work has identified clustering of the active trachoma sign “trachomatous inflammation—follicular” (TF), there is limited understanding of the spatial structure of trachomatous trichiasis (TT), the rarer, end-stage, blinding form of disease. Here we use community-level TF prevalence, information on access to water and sanitation, and large-scale environmental and socio-economic indicators to model the spatial variation in community-level TT prevalence in Benin, Cote d’Ivoire, DRC, Guinea, Ethiopia, Malawi, Mozambique, Nigeria, Sudan and Uganda. Methods We fit binomial mixed models, with community-level random effects, separately for each country. In countries where spatial correlation was detected through a semi-variogram diagnostic check we then fitted a geostatistical model to the TT prevalence data including TF prevalence as an explanatory variable. Results The estimated regression relationship between community-level TF and TT was significant in eight countries. We estimate that a 10% increase in community-level TF prevalence leads to an increase in the odds for TT ranging from 20 to 86% when accounting for additional covariates. Conclusion We find evidence of an association between TF and TT in some parts of Africa. However, our results also suggest the presence of additional, country-specific, spatial risk factors which modulate the variation in TT risk.

Abstract Background Despite significant declines in under five mortality (U5M) over the last 3 decades, Kenya did not achieve Millennium Development Goal 4 (MDG 4) by 2015. To better understand trends and inequalities in child mortality, analysis of U5M variation at subnational decision making units is required. Here the comprehensive compilation and analysis of birth history data was used to understand spatio-temporal variation, inequalities and progress towards achieving the reductions targets of U5M between 1965 and 2013 and projected to 2015 at decentralized health planning units (counties) in Kenya. Methods Ten household surveys and three censuses with data on birth histories undertaken between 1989 and 2014 were assembled. The birth histories were allocated to the respective counties and demographic methods applied to estimate U5M per county by survey. To generate a single U5M estimate for year and county, a Bayesian spatio-temporal Gaussian process regression was fitted accounting for variation in sample size, surveys and demographic methods. Inequalities and the progress in meeting the goals set to reduce U5M were evaluated subnationally. Results Nationally, U5M reduced by 61·6%, from 141·7 (121·6–164·0) in 1965 to 54·5 (44·6–65·5) in 2013. The declining U5M was uneven ranging between 19 and 80% across the counties with some years when rates increased. By 2000, 25 counties had achieved the World Summit for Children goals. However, as of 2015, no county had achieved MDG 4. There was a striking decline in the levels of inequality between counties over time, however, disparities persist. By 2013 there persists a 3·8 times difference between predicted U5M rates when comparing counties with the highest U5M rates against those with the lowest U5M rates. Conclusion Kenya has made huge progress in child survival since independence. However, U5M remains high and heterogeneous with substantial differences between counties. Better use of the current resources through focused allocation is required to achieve further reductions, reduce inequalities and increase the likelihood of achieving Sustainable Development Goal 3·2 on U5M by 2030.

Background The diagnosis of lower urinary tract symptoms related to suspected bladder outflow obstruction from benign prostate hyperplasia/enlargement in men is increasing. This is leading to high demand on healthcare services; however, there is limited knowledge of differences in pharmacotherapy prescribing for this condition based on geography.Objective To investigate potential variation in drug prescribing for suspected bladder outflow obstruction in Scotland, based on analysis of publicly available data, to identify trends and inform future prescribing.Study design A longitudinal register-based data study of prescribing and patient data publicly available from Scottish registries. All information is available as monthly aggregates at the level of single general practices.Setting and participants 903 (97%) general practices in Scotland, over a 50-month period (October 2015 to November 2019).Outcome measurements and statistical analysis We analysed numbers of daily doses of drugs for suspected bladder outflow obstruction prescribed per month using a Bayesian Poisson regression analysis, incorporating random effects to account for spatial and temporal elements.Results Prescriptions for suspected bladder outflow obstruction medications increased during the observation period (overall average rate of change 1.24±0.28, ranging from 0.893 in Orkney to 1.95 in Lanarkshire). While some determinants of health inequality regarding prescribing practices across health boards are consistent with those known from the literature, other inequalities remain unexplained after accounting for practice-specific and patient-specific characteristics such as deprivation and rurality.Conclusions Inequalities in prescribing for suspected bladder outflow obstruction medications exist in Scotland, partially ascribable to accepted sociodemographic and geographic factors.

User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.

IntroductionThe decision-making process for malaria control and elimination strategies has become more challenging. Interventions need to be targeted at council level to allow for changing malaria epidemiology and an increase in the number of possible interventions. Models of malaria dynamics can support this process by simulating potential impacts of multiple interventions in different settings and determining appropriate packages of interventions for meeting specific expected targets.MethodsThe OpenMalaria model of malaria dynamics was calibrated for all 184 councils in mainland Tanzania using data from malaria indicator surveys, school parasitaemia surveys, entomological surveillance, and vector control deployment data. The simulations were run for different transmission intensities per region and five interventions, currently or potentially included in the National Malaria Strategic Plan, individually and in combination. The simulated prevalences were fitted to council specific prevalences derived from geostatistical models to obtain council specific predictions of the prevalence and number of cases between 2017 and 2020. The predictions were used to evaluate in silico the feasibility of the national target of reaching a prevalence of below 1% by 2020, and to suggest alternative intervention stratifications for the country.ResultsThe historical prevalence trend was fitted for each council with an agreement of 87% in 2016 (95%CI: 0.84-0.90) and an agreement of 90% for the historical trend (2003-2016) (95%CI: 0.87-0.93) The current national malaria strategy was expected to reduce the malaria prevalence between 2016 and 2020 on average by 23.8% (95% CI: 19.7%-27.9%) if current case management levels were maintained, and by 52.1% (95% CI: 48.8%-55.3%) if the case management were improved. Insecticide treated nets and case management were the most cost-effective interventions, expected to reduce the prevalence by 25.0% (95% CI: 19.7%-30.2) and to avert 37 million cases between 2017 and 2020. Mass drug administration was included in most councils in the stratification selected for meeting the national target at minimal costs, expected to reduce the prevalence by 77.5% (95%CI: 70.5%-84.5%) and to avert 102 million cases, with almost twice higher costs than those of the current national strategy. In summary, the model suggested that current interventions are not sufficient to reach the national aim of a prevalence of less than 1% by 2020 and a revised strategic plan needs to consider additional, more effective interventions, especially in high transmission areas and that the targets need to be revisited.ConclusionThe methodology reported here is based on intensive interactions with the NMCP and provides a helpful tool for assessing the feasibility of country specific targets and for determining which intervention stratifications at sub-national level will have most impact. This country-led application could support strategic planning of malaria control in many other malaria endemic countries.

Every year, 435,000 people worldwide die from Malaria, mainly in Africa and Asia. However, malaria is a curable and preventable disease. Most countries are developing malaria elimination plans to meet sustainable development goal three, target 3.3, which includes ending the epidemic of malaria by 2030. Rwanda, through the malaria strategic plan 2012-2018 set a target to reduce malaria incidence by 42% from 2012 to 2018. Assessing the health policy and taking a decision using the incidence rate approach is becoming more challenging. We are proposing suitable statistical methods that handle spatial structure and uncertainty on the relative risk that is relevant to National Malaria Control Program. We used a spatio-temporal model to estimate the excess probability for decision making at a small area on evaluating reduction of incidence. SIR and BYM models were developed using routine data from Health facilities for the period from 2012 to 2018 in Rwanda. The fitted model was used to generate relative risk (RR) estimates comparing the risk with the malaria risk in 2012, and to assess the probability of attaining the set target goal per area. The results showed an overall increase in malaria in 2013 to 2018 as compared to 2012. Ofall sectors in Rwanda, 47.36% failed to meet targeted reduction in incidence from 2012 to 2018. Our approach of using excess probability method to evaluate attainment of target or identifying threshold is a relevant statistical method, which will enable the Rwandan Government to sustain malaria control and monitor the effectiveness of targeted interventions.

Abstract Background Despite the biological plausibility of hotspots fueling malaria transmission, the evidence to support this concept has been mixed. If transmission spreads from high burden to low burden households in a consistent manner, then this could have important implications for control and elimination program development. Methods Data from a longitudinal cohort in The Gambia was analyzed. All consenting individuals residing in 12 villages across the country were sampled monthly from June (dry season) to December 2013 (wet season), in April 2014 (mid dry season), and monthly from June to December 2014. A study nurse stationed within each village recorded passively detected malaria episodes between visits. Plasmodium falciparum infections were determined by polymerase chain reaction and analyzed using a geostatistical model. Results Household-level observed monthly incidence ranged from 0 to 0.50 infection per person (interquartile range = 0.02–0.10) across the sampling months, and high burden households exist across all study villages. There was limited evidence of a spatio-temporal pattern at the monthly timescale irrespective of transmission intensity. Within-household transmission was the most plausible hypothesis examined to explain the observed heterogeneity in infections. Conclusions Within-village malaria transmission patterns are concentrated in a small proportion of high burden households, but patterns are stochastic regardless of endemicity. Our findings support the notion of transmission occurring at the household and village scales but not the use of a targeted approach to interrupt spreading of infections from high to low burden areas within villages in this setting.

Abstract Background Spatial and temporal malaria risk maps are essential tools to monitor the impact of control, evaluate priority areas to reorient intervention approaches and investments in malaria endemic countries. Here, the analysis of 36 years data on Plasmodium falciparum prevalence is used to understand the past and chart a future for malaria control in Kenya by confidently highlighting areas within important policy relevant thresholds to allow either the revision of malaria strategies to those that support pre-elimination or those that require additional control efforts. Methods Plasmodium falciparum parasite prevalence (PfPR) surveys undertaken in Kenya between 1980 and 2015 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2–10 years (PfPR2–10) at 1 × 1 km spatial resolution from 1990 to 2015. Changing PfPR2–10 was compared against plausible explanatory variables. The fitted model was used to categorize areas with varying degrees of prediction probability for two important policy thresholds PfPR2–10

Abstract Background Undernutrition among children under 5 years of age continues to be a public health challenge in many low- and middle-income countries and can lead to growth stunting. Infectious diseases may also affect child growth, however their actual impact on the latter can be difficult to quantify. In this paper, we analyse data from 20 Demographic and Health Surveys (DHS) conducted in 13 African countries to investigate the relationship between malaria and stunting. Our objective is to make inference on the association between malaria incidence during the first year of life and height-for-age Z-scores (HAZs). Methods We develop a geostatistical model for HAZs as a function of both measured and unmeasured child-specific and spatial risk factors. We visualize stunting risk in each of the 20 analysed surveys by mapping the predictive probability that HAZ is below − 2. Finally, we carry out a meta-analysis by modelling the estimated effects of malaria incidence on HAZ from each DHS as a linear regression on national development indicators from the World Bank. Results A non-spatial univariate linear regression of HAZ on malaria incidence showed a negative association in 18 out of 20 surveys. However, after adjusting for spatial risk factors and controlling for confounding effects, we found a weaker association between HAZ and malaria, with a mix of positive and negative estimates, of which 3 out of 20 are significantly different from zero at the conventional 5% level. The meta-analysis showed that this variation in the estimated effect of malaria incidence on HAZ is significantly associated with the amount of arable land. Conclusion Confounding effects on the association between malaria and stunting vary both by country and over time. Geostatistical analysis provides a useful framework that allows to account for unmeasured spatial confounders. Establishing whether the association between malaria and stunting is causal would require longitudinal follow-up data on individual children.

Abstract Background Countries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant. Methods A spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates. Results By 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR2–10 and, by 2014, 17 regions were below 5% PfPR2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being

Slum residents are more vulnerable to COVID-19 infection. Without a specific treatment, vaccination became the main strategy against COVID-19. In this study, we determined the rate and factors associated with the willingness to get vaccinated against COVID-19 among slum residents and their main reasons associated with the vaccine intention. The study was conducted in Pau da Lima, a slum community in Salvador Brazil. In total, 985 residents were interviewed. Among them 66.0% (650/985) were willing to get vaccinated, 26.1% (257/985) were hesitant to take the vaccine and 7.9% (78/285) were not sure. The main reasons cited for vaccine hesitancy or being unsure were concerns about vaccine efficacy and potential side effects. In contrast, the main reasons cited for wanting the vaccine were the high incidence of COVID-19 cases and participants’ self-perception of their own health history. Multivariate analysis identified that COVID-19 vaccine hesitancy was associated with younger age and low social capital, summarized as low perceived importance of vaccination to protect one’s family, friends and community. Slum residents have been less willing to vaccinate than the general population. Social capital presents a critical opportunity in the design of communication campaigns to increase COVID-19 vaccine acceptance in slum settings.

Abstract Background Spatial determinants of malaria risk within communities are associated with heterogeneity of exposure to vector mosquitoes. The abundance of adult malaria vectors inside people’s houses, where most transmission takes place, should be associated with several factors: proximity of houses to larval habitats, structural characteristics of houses, indoor use of vector control tools containing insecticides, and human behavioural and environmental factors in and near houses. While most previous studies have assessed the association of larval habitat proximity in landscapes with relatively low densities of larval habitats, in this study these relationships were analysed in a region of rural, lowland western Kenya with high larval habitat density. Methods 525 houses were sampled for indoor-resting mosquitoes across an 8 by 8 km study area using the pyrethrum spray catch method. A predictive model of larval habitat location in this landscape, previously verified, provided derivations of indices of larval habitat proximity to houses. Using geostatistical regression models, the association of larval habitat proximity, long-lasting insecticidal nets (LLIN) use, house structural characteristics (wall type, roof type), and peridomestic variables (cooking in the house, cattle near the house, number of people sleeping in the house) with mosquito abundance in houses was quantified. Results Vector abundance was low (mean, 1.1 adult Anopheles per house). Proximity of larval habitats was a strong predictor of Anopheles abundance. Houses without an LLIN had more female Anopheles gambiae s.s., Anopheles arabiensis and Anopheles funestus than houses where some people used an LLIN (rate ratios, 95% CI 0.87, 0.85–0.89; 0.84, 0.82–0.86; 0.38, 0.37–0.40) and houses where everyone used an LLIN (RR, 95% CI 0.49, 0.48–0.50; 0.39, 0.39–0.40; 0.60, 0.58–0.61). Cooking in the house also reduced Anopheles abundance across all species. The number of people sleeping in the house, presence of cattle near the house, and house structure modulated Anopheles abundance, but the effect varied with Anopheles species and sex. Conclusions Variation in the abundance of indoor-resting Anopheles in rural houses of western Kenya varies with clearly identifiable factors. Results suggest that LLIN use continues to function in reducing vector abundance, and that larval source management in this region could lead to further reductions in malaria risk by reducing the amount of an obligatory resource for mosquitoes near people’s homes.

In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced prevalence data, including both classical maximum likelihood and Bayesian approaches to parameter estimation and plug-in or Bayesian prediction. More specifically, the new package implements fitting of geostatistical models for binomial data, based on two distinct approaches. The first approach uses a generalized linear mixed model with logistic link function, binomial error distribution and a Gaussian spatial process as a stochastic component in the linear predictor. A simpler, but approximate, alternative approach consists of fitting a linear Gaussian model to empirical-logit-transformed data. The package also includes implementations of convolution-based low-rank approximations to the Gaussian spatial process to enable computationally efficient analysis of large spatial datasets. We illustrate the use of the package through the analysis of Loa loa prevalence data from Cameroon and Nigeria. We illustrate the use of the low rank approximation using a simulated geostatistical dataset.

Background: The prevalence of malaria infection in time and space provides important information on the likely sub-national epidemiology of malaria burdens and how this has changed following intervention. Model-based geostatitics (MBG) allow national malaria control programmes to leverage multiple data sources to provide predictions of malaria prevalance by district over time. These methods are used to explore the possible changes in malaria prevalance in Malawi from 2010 to 2017. Methods: Plasmodium falciparum parasite prevalence (PfPR) surveys undertaken in Malawi between 2000 and 2017 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2–10 years (PfPR2–10) at 1×1 km spatial resolutions. Parameter estimation was carried out using the Monte Carlo maximum likelihood methods. Population-adjusted prevalence and populations at risk by district were calculated for 2010 and 2017 to inform malaria control program priority setting. Results: 2,237 surveys at 1,834 communities undertaken between 2000 and 2017 were identified, geo-coded and used within the MBG framework to predict district malaria prevalence properties for 2010 and 2017. Nationally, there was a 47.2% reduction in the mean modelled PfPR2-10 from 29.4% (95% confidence interval (CI) 26.6 to 32.3%) in 2010 to 15.2% (95% CI 13.3 to 18.0%) in 2017. Declining prevalence was not equal across the country, 25 of 27 districts showed a substantial decline ranging from a 3.3% reduction to 79% reduction. By 2017, 16% of Malawi’s population still lived in areas that support PfPR2-10 ≥ 25%. Conclusions: Malawi has made substantial progress in reducing the prevalence of malaria over the last seven years. However, Malawi remains in meso-endemic malaria transmission risk. To sustain the gains made and continue reducing the transmission further, universal control interventions need to be maintained at a national level.

Summary: Background: Podoconiosis is a type of tropical lymphoedema that causes massive swelling of the lower limbs. The disease is associated with both economic insecurity, due to long-term morbidity-related loss of productivity, and intense social stigma. Reliable and detailed data on the prevalence and distribution of podoconiosis are scarce. We aimed to fill this data gap by doing a nationwide community-based study to estimate the number of cases throughout Rwanda. Methods: We did a population-based cross-sectional survey to determine the national prevalence of podoconiosis. A podoconiosis case was defined as a person with bilateral, asymmetrical lymphoedema of the lower limb present for more than 1 year, who tested negative for Wuchereria bancrofti antigen (determined by Filariasis Test Strip) and specific IgG4 (determined by Wb123 test), and had a history of any of the associated clinical signs and symptoms. All adults (aged ≥15 years) who resided in any of the 30 districts of Rwanda for 10 or more years were invited at the household level to participate. Participants were interviewed and given a physical examination before Filariasis Test Strip and Wb123 testing. We fitted a binomial mixed model combining the site-level podoconiosis prevalence with continuous environmental covariates to estimate prevalence at unsampled locations. We report estimates of cases by district combining our mean predicted prevalence and a contemporary gridded map of estimated population density. Findings: Between June 12, and July 28, 2017, 1 360 612 individuals—719 730 (53%) women and 640 882 (47%) men—were screened from 80 clusters in 30 districts across Rwanda. 1143 individuals with lymphoedema were identified, of whom 914 (80%) had confirmed podoconiosis, based on the standardised diagnostic algorithm. The overall prevalence of podoconiosis was 68·5 per 100 000 people (95% CI 41·0–109·7). Podoconiosis was found to be widespread in Rwanda. District-level prevalence ranged from 28·3 per 100 000 people (16·8–45·5, Nyarugenge, Kigali province) to 119·2 per 100 000 people (59·9–216·2, Nyamasheke, West province). Prevalence was highest in districts in the North and West provinces: Nyamasheke, Rusizi, Musanze, Nyabihu, Nyaruguru, Burera, and Rubavu. We estimate that 6429 (95% CI 3938–10 088) people live with podoconiosis across Rwanda. Interpretation: Despite relatively low prevalence, podoconiosis is widely distributed geographically throughout Rwanda. Many patients are likely to be undiagnosed and morbidity management is scarce. Targeted interventions through a well coordinated health system response are needed to manage those affected. Our findings should inform national level planning, monitoring, and implementation of interventions. Funding: Wellcome Trust.

As described by the strong academic literature, (Vattimo, Bauman, Mumford, Simon, Haraway, Meschiari, Florida) the contemporary society is going through new challenges, such as the friction between youth, technology, and productivity. These challenges affect the way people live and experience the cities, but also the way cities need to evolve. An anthological analysis and a study of secondary sources is used to analyze the new spatial and social experiences, while the analysis of Milan (Italy) as a case study of a creative city is used to understand the rapid shift towards the virtualization of cities, in which consumption is progressively induced by a projected image of the city rather than its actual physical fabric. This manuscript opens a research front, with the goal to understand how architecture and urban design should leave the traditional typologies to propose a new way of creating and living architecture, caught in the middle between the real and the virtual.

Podoconiosis is a non-filarial elephantiasis, which causes massive swelling of the lower legs. It was identified as a neglected tropical disease by WHO in 2011. Understanding of the geographical distribution of the disease is incomplete. As part of a global mapping of podoconiosis, this study was conducted in Cameroon to map the distribution of the disease. This mapping work will help to generate data on the geographical distribution of podoconiosis in Cameroon and contribute to the global atlas of podoconiosis.We used a multi-stage sampling design with stratification of the country by environmental risk of podoconiosis. We sampled 76 villages from 40 health districts from the ten Regions of Cameroon. All individuals of 15-years old or older in the village were surveyed house-to-house and screened for lymphedema. A clinical algorithm was used to reliably diagnose podoconiosis, excluding filarial-associated lymphedema. Individuals with lymphoedema were tested for circulating Wuchereria bancrofti antigen and specific IgG4 using the Alere Filariasis Test Strips (FTS) test and the Standard Diagnostics (SD) BIOLINE lymphatic filariasis IgG4 test (Wb123) respectively, in addition to thick blood films. Presence of DNA specific to W. bancrofti was checked on night blood using a qPCR technique.Overall, 10,178 individuals from 4,603 households participated in the study. In total, 83 individuals with lymphedema were identified. Of the 83 individuals with lymphedema, two were found to be FTS positive and all were negative using the Wb123 test. No microfilaria of W. bancrofti were found in the night blood of any individual with clinical lymphedema. None were found to be positive for W. bancrofti using qPCR. Of the two FTS positive cases, one was positive for Mansonella perstans DNA, while the other harbored Loa loa microfilaria. Overall, 52 people with podoconiosis were identified after applying the clinical algorithm. The overall prevalence of podoconiosis was found to be 0.5% (95% [confidence interval] CI; 0.4-0.7). At least one case of podoconiosis was found in every region of Cameroon except the two surveyed villages in Adamawa. Of the 40 health districts surveyed, 17 districts had no cases of podoconiosis; in 15 districts, mean prevalence was between 0.2% and 1.0%; and in the remaining eight, mean prevalence was between 1.2% and 2.7%.Our investigation has demonstrated low prevalence but almost nationwide distribution of podoconiosis in Cameroon. Designing a podoconiosis control program is a vital next step. A health system response to the burden of podoconiosis is important, through case surveillance and morbidity management services.

Background: In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the magnitude of podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia. Methods: We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 communities in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015. Results: Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People’s [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent. Conclusions: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.

Background: Four million people die each year from diseases caused by exposure to household air pollution. There is an association between exposure to household air pollution and pneumonia in children (half a million attributable deaths a year); however, whether this is true in adults is unknown. We conducted a case-control study in urban Malawi to examine the association between exposure to household air pollution and pneumonia in adults. Methods: Hospitalized patients with radiologically confirmed pneumonia (cases) and healthy community controls underwent 48 hours of ambulatory and household particulate matter (µg/m3) and carbon monoxide (ppm) exposure monitoring. Multivariate logistic regression, stratified by HIV status, explored associations between these and other potential risk factors with pneumonia. Results: 145 (117 HIV-positive; 28 HIV-negative) cases and 253 (169 HIV-positive; 84 HIV-negative) controls completed follow up. We found no evidence of association between household air pollution exposure and pneumonia in HIV-positive (e.g. ambulatory particulate matter adjusted odds ratio [aOR] 1.00 [95% CI 1.00–1.01, p=0.141]) or HIV-negative (e.g. ambulatory particulate matter aOR 1.00 [95% CI 0.99–1.01, p=0.872]) participants. Chronic respiratory disease was associated with pneumonia in both HIV-positive (aOR 28.07 [95% CI 9.29–84.83, p

Background: In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia. Methods: We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015. Results: Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People’s [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent. Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.

The need for surveillance systems generating targeted, data-driven, responsive control efforts to accelerate and sustain malaria transmission reduction has been emphasized by programme managers, policy makers and scientists. Surveillance using easy-to-access population subgroups (EAGs) may result in considerable cost saving compared to household surveys as the identification and selection of individuals to be surveyed is simplified, fewer personnel are needed, and logistics are simpler. We reviewed available literature on the validation of estimates of key indicators of malaria control progress derived from EAGs, and describe the options to deal with the context specific bias that may occur.A literature search was conducted of all documents reporting validation of estimates of malaria control indicators from EAG surveys before the 31st of December 2016. Additional records were identified through cross-reference from selected records, other applicable policy documents and grey literature. After removal of duplicates, 13, 180 abstracts were evaluated and 2,653 eligible abstracts were identified mentioning surveillance in EAGs, of which 29 full text articles were selected for detailed review. The nine articles selected for systematic review compared estimates from health facility and school surveys with those of a contemporaneous sample of the same population in the same geographic area.Review of the available literature on EAGs suitable for surveillance of malaria control progress revealed that little effort has been made to explore the potential approach and settings for use of EAGs; and that there was wide variation in the precision of estimates of control progress between and within studies, particularly for estimates of control intervention coverage. Only one of the studies evaluated the geospatial representativeness of EAG samples, or carried out geospatial analyses to assess or control for lack of geospatial representativeness. Two studies attempted to measure the degree of bias or improve the precision of estimates by controlling for bias in a multivariate analysis; and this was only successful in one study. The observed variability in accuracy of estimates is likely to be caused by selection and/or information bias due to the inherent nature of EAGs. The reviewed studies provided insight into the design and analytical approaches that could be used to limit bias.The utility EAGs for routine surveillance of progress in malaria control at the district or sub-district programmatic level will be driven by several factors including whether serial point estimates to measure transmission reduction or more precise geospatial distribution to track 'hot-spots' is required, the acceptable degree of precision, the target population, and the resources available for surveillance. The opportunities offered by novel geostatistical analyses and hybrid sampling frames to overcome bias justify a renewed exploration of use of EAGs for malaria monitoring and evaluation.

Abstract Background The original aim of the African Programme for Onchocerciasis Control (APOC) was to control onchocerciasis as a public health problem in 20 African countries. In order to identify all high risk areas where ivermectin treatment was needed to achieve control, APOC used Rapid Epidemiological Mapping of Onchocerciasis (REMO). REMO involved spatial sampling of villages to be surveyed, and examination of 30 to 50 adults per village for palpable onchocercal nodules. REMO has now been virtually completed and we report the results in two articles. A companion article reports the delineation of high risk areas based on expert analysis. The present article reports the results of a geostatistical analysis of the REMO data to map endemicity levels and estimate the number infected. Methods A model-based geostatistical analysis of the REMO data was undertaken to generate high-resolution maps of the predicted prevalence of nodules and of the probability that the true nodule prevalence exceeds the high risk threshold of 20%. The number infected was estimated by converting nodule prevalence to microfilaria prevalence, and multiplying the predicted prevalence for each location with local data on population density. The geostatistical analysis included the nodule palpation data for 14,473 surveyed villages. Results The generated map of onchocerciasis endemicity levels, as reflected in the prevalence of nodules, is a significant advance with many new endemic areas identified. The prevalence of nodules was > 20% over an area of 2.5 million km2 with an estimated population of 62 million people. The results were consistent with the delineation of high risk areas of the expert analysis except for borderline areas where the prevalence fluctuated around 20%. It is estimated that 36 million people would have been infected in the APOC countries by 2011 if there had been no ivermectin treatment. Conclusions The map of onchocerciasis endemicity levels has proven very valuable for onchocerciasis control in the APOC countries. Following the recent shift to onchocerciasis elimination, the map continues to play an important role in planning treatment, evaluating impact and predicting treatment end dates in relation to local endemicity levels.

BACKGROUND:There is a paucity of robust epidemiological data on snakebite, and data available from hospitals and localized or time-limited surveys have major limitations. No study has investigated the incidence of snakebite across a whole country. We undertook a community-based national survey and model based geostatistics to determine incidence, envenoming, mortality and geographical pattern of snakebite in Sri Lanka. METHODOLOGY/PRINCIPAL FINDINGS:The survey was designed to sample a population distributed equally among the nine provinces of the country. The number of data collection clusters was divided among districts in proportion to their population. Within districts clusters were randomly selected. Population based incidence of snakebite and significant envenoming were estimated. Model-based geostatistics was used to develop snakebite risk maps for Sri Lanka. 1118 of the total of 14022 GN divisions with a population of 165665 (0.8%of the country's population) were surveyed. The crude overall community incidence of snakebite, envenoming and mortality were 398 (95% CI: 356-441), 151 (130-173) and 2.3 (0.2-4.4) per 100000 population, respectively. Risk maps showed wide variation in incidence within the country, and snakebite hotspots and cold spots were determined by considering the probability of exceeding the national incidence. CONCLUSIONS/SIGNIFICANCE:This study provides community based incidence rates of snakebite and envenoming for Sri Lanka. The within-country spatial variation of bites can inform healthcare decision making and highlights the limitations associated with estimates of incidence from hospital data or localized surveys. Our methods are replicable, and these models can be adapted to other geographic regions after re-estimating spatial covariance parameters for the particular region.